The data that we collect from end-users can be just as valuable as the functionality that we deliver to end-users. But do you think through data requirements as carefully as you think through functional requirements? Given the pressure to deliver quality functionality, it’s easy to forget that data quality is another important dimension of software quality, and that data requirements are a distinct class of requirements worthy of analysis in their own right. With the costs of data collection falling and the power of data analysis tools climbing, getting data requirements right is growing in importance.
So, how do we get data requirements right? How do we identify what data we need to acquire now so that we have the information we need to make good decisions later? How should we store that data in order to ensure that it supports analytics? This paper will describe a few types of data issues and present real-world examples which illustrate their business impact. Our hope is that reading this paper will help you to appreciate how data quality contributes to software quality, and equip you with a set of tools for capturing better data.
Target audience: Intermediate